Integrating across neuroimaging modalities boosts prediction accuracy of cognitive ability
Fig 1
Stacking methodology for multi-modal data prediction.
In the first step and for each brain measurement, a 5-fold cross-validation is applied to the training set to simultaneously optimize a LASSO-PCR model and produce out-of-sample training set predictions. The optimized trained LASSO-PCR model is then used to generate predictions from the test set. In the second learning step, training and test set predictions are stacked across channels. A new LASSO model acting on the new training set matrix is then optimized with an inner 5-fold cross-validation and fitted to generate the final predictions on the new test set.